Metabolise

Inspiration

Wearable fitness tracking tech is on the rise, but are we using it right? Take calorie tracking for example, the process is quite broken: we wear fitbits and track calorie expenditures from the Fitbit app, then we switch to MyFitnesspal to then enter in every meal we had, only to compute our calorific deficit. Calorie tracking should be a closed loop, done in one place without all of this hassle.

Meal entry is also done badly. Myfitnesspal, the most popular food logging app, queries against a static, rigid and incomplete database while asking you if you just had a salad of 1.0ml or 710.0ml.

This is why we built Metabolise, for an easy fitness tracking experience all in a closed loop.

What it does

Real time tracking of your daily and weekly net calorie intake, with an incredibly simple UI that tells you exactly what you need to know in a half-second glance.

Accurate natural language processing that knows exactly what you mean when you say that you just ate a “cheese pizza”. ANY description would just work!

A rigorously trained model to calculate accurate calorie estimations. Metabolise proactively suggests the most relevant unit of measurement from what you’ve said (in live) and doesn’t mess about with units you don’t have time for. This means no nonsensical units like "g", "ml" or "oz". Only units that really make sense!

How we built it

We built the frontend with iOS in Swift, using custom made UI views as well as Nuance which powers voice recognition.

We built the backend with Flask and the natural language processing model with scikit-learn.